Search Results for "deep learning" - Page 6
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Showing 157 open source projects for "deep learning"
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X-DeepLearning
An industrial deep learning framework for high-dimension sparse data
X-DeepLearning (XDL for short) is a complete set of deep optimization solutions for high-dimensional sparse data scenarios (such as advertising/recommendation/search, etc.). XDL version 1.2 has been released recently. Performance optimization for large batch/low concurrency scenarios, 50-100% performance improvement in such scenarios. Storage and communication optimization, parameters are automatically allocated globally without manual intervention, and requests are merged to completely... -
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benchm-ml
A benchmark of commonly used open source implementations
...The benchmarks cover algorithms like logistic regression, random forest, gradient boosting, and deep neural networks, and they compare across toolkits such as scikit-learn, R packages, xgboost, H2O, Spark MLlib, etc. The repository is structured in logical folders, each corresponding to algorithm categories. -
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pytorch-examples
Simple examples to introduce PyTorch
The pytorch-examples project is a collection of concise and practical examples demonstrating how to use PyTorch for machine learning and deep learning tasks. It focuses on clarity and minimalism, providing small, self-contained scripts that illustrate key concepts such as neural network training, optimization, and data handling. The examples cover a range of topics including supervised learning, generative models, and reinforcement learning, making it a valuable resource for both beginners and experienced practitioners. ... -
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TenorSpace.js
Neural network 3D visualization framework
TensorSpace is a neural network 3D visualization framework built using TensorFlow.js, Three.js and Tween.js. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information. After preprocessing the model, TensorSpace supports the visualization of pre-trained models from TensorFlow, Keras and TensorFlow.js. ... -
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
Data Science IPython Notebooks is a broad, curated set of Jupyter notebooks covering Python, data wrangling, visualization, machine learning, deep learning, and big data tools. It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. Advanced sections touch on neural networks and distributed computing topics, helping you bridge from basics to production-adjacent workflows. ... -
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NN-SVG
Publication-ready NN-architecture schematics
Illustrations of Neural Network architectures are often time-consuming to produce, and machine learning researchers all too often find themselves constructing these diagrams from scratch by hand. NN-SVG is a tool for creating Neural Network (NN) architecture drawings parametrically rather than manually. It also provides the ability to export those drawings to Scalable Vector Graphics (SVG) files, suitable for inclusion in academic papers or web pages. -
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NimTorch
PyTorch - Python + Nim
NimTorch is a deep learning library for the Nim programming language, providing bindings to PyTorch for efficient tensor computations and neural network functionalities. -
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understand-plugin-framework
Demos to help understand plugin framwork
understand-plugin-framework is an educational project that explores how plugin frameworks operate within Android applications. It demonstrates how applications can dynamically load and execute external modules without requiring installation through standard mechanisms. The repository provides examples and explanations of class loading, resource management, and component integration. It is designed to help developers understand the internal architecture of plugin-based systems. The project is... -
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Tiramisu
Polyhedral compiler for expressing fast and portable data algorithms
...It provides a simple C++ API for expressing algorithms (Tiramisu expressions) and how these algorithms should be optimized by the compiler. Tiramisu can be used in areas such as linear and tensor algebra, deep learning, image processing, stencil computations and machine learning. The Tiramisu compiler is based on the polyhedral model thus it can express a large set of loop optimizations and data layout transformations. Currently, it targets (1) multicore X86 CPUs, (2) Nvidia GPUs, (3) Xilinx FPGAs (Vivado HLS) and (4) distributed machines (using MPI). ... -
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cnn-text-classification-tf
Convolutional Neural Network for Text Classification in Tensorflow
...The project includes scripts for training, evaluation, and data handling, making it easy to run experiments on datasets such as movie reviews or other labeled text collections. By breaking down the model into understandable components, it serves as a practical reference for students and practitioners learning how deep learning models handle text beyond traditional bag-of-words approaches. -
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Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a folder of images from the command line. It could even do real-time face recognition and blur faces on videos when used with other Python libraries. -
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Keras resources
Directory of tutorials and open-source code repositories
...It also includes links to external projects built with Keras, demonstrating real-world applications of deep learning techniques. The structure is designed for easy navigation, allowing users to quickly find relevant materials based on their skill level or area of interest. It serves as both a learning pathway and a reference library for ongoing development. -
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Mocha.jl
Deep Learning framework for Julia
Mocha.jl is a deep learning framework for Julia, inspired by the C++ Caffe framework. It offers efficient implementations of gradient descent solvers and common neural network layers, supports optional unsupervised pre-training, and allows switching to a GPU backend for accelerated performance. The development of Mocha.jl happens in relative early days of Julia. -
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Zhao
A compilation of "The Princely Party Relationship Network"
zhao is a repository that consolidates research, data, and insights related to Zhao, which is likely an individual’s research collection, notes, or curated resources on deep learning, AI, or computational topics (name and content context suggest specialized study). The project may include code examples, experiment results, references to academic papers, mathematical notes, and supporting scripts to explore specific ML methods, benchmarks, or theoretical findings. Because it aggregates content associated with Zhao, the repository functions as a personal or shared knowledge base for readers who want insight into a body of research rather than a traditional software library. ... -
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Edward
A probabilistic programming language in TensorFlow
...Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard. ... -
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PyTorch Book
PyTorch tutorials and fun projects including neural talk
This is the corresponding code for the book "The Deep Learning Framework PyTorch: Getting Started and Practical", but it can also be used as a standalone PyTorch Getting Started Guide and Tutorial. The current version of the code is based on pytorch 1.0.1, if you want to use an older version please git checkout v0.4or git checkout v0.3. Legacy code has better python2/python3 compatibility, CPU/GPU compatibility test. -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. ... -
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Image classification models for Keras
Keras code and weights files for popular deep learning models
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'... -
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Deeplearning-papernotes
Summaries and notes on Deep Learning research papers
Deeplearning-papernotes is an implementation of Convolutional Neural Networks for sentence and text classification in TensorFlow, based on a well-known research paper that applies CNN architectures to natural language processing tasks with strong performance in sentiment analysis and similar classification problems. The repository provides the complete network definition, including an embedding layer to convert words into dense representations, convolution and max-pooling layers to extract... -
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cnn-benchmarks
Benchmarks for popular CNN models
...Overall, cnn-benchmarks is a practical tool for performance analysis in deep learning workflows. -
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Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework
Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind. Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform libraries. ... -
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Accord.NET Framework
Scientific computing, machine learning and computer vision for .NET
The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono. -
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TensorFlow World
Simple and ready-to-use tutorials for TensorFlow
...The explanations are present in the wiki associated with this repository. There are different motivations for this open source project. TensorFlow (as we write this document) is one of / the best deep learning frameworks available. The question that should be asked is why has this repository been created when there are so many other tutorials about TensorFlow available on the web? Deep Learning is in very high interest these days - there's a crucial need for rapid and optimized implementations of the algorithms and architectures. ... -
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fast-neural-style
Feedforward style transfer
...The repository includes training scripts, pre-trained models, and examples demonstrating how to apply styles efficiently. It also provides insights into the underlying techniques used in neural style transfer, making it both a practical tool and a learning resource. By combining performance and quality, it enables creative applications in image processing and design. Overall, fast-neural-style showcases how deep learning can be used for real-time artistic transformations. -
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Caffe
A fast open framework for deep learning
Caffe is an open source deep learning framework that’s focused on expression, speed and modularity. It’s got an expressive architecture that encourages application and innovation, and extensible code that’s great for active development. Caffe also offers great speed, capable of processing over 60M images per day with a single NVIDIA K40 GPU. It’s arguably one of the fastest convnet implementations around.
